A Navigation Signals Monitoring, Analysis and Recording Tool: Application to Real-Time Interference Detection and Classification

Given the extensive dependency on Global Navigation Satellite Systems (GNSS) for several crucial applications, the disruption caused by intentional or unintentional Radio Frequency Interference (RFI) may dramatically affect reliability and poses potential threats to various operations dependent on such systems. Recently, these threats have increased, and their detection and mitigation are of utmost importance in the field. To this aim, this paper presents an architecture for real-time detection and classification of RFI affecting multi-band GNSS signals based on a machine learning method. This study proposes an architecture combining an actual GNSS monitoring station for recording GNSS signals (Navigation Signals Monitoring, Analysis, and Recording Tool (N-SMART) system) with a deep neural network approach to detect and classify different classes of interferences. The proposed approach enables continuous monitoring, recording, and prompt alerting of RFI occurrences in multi-band GNSS signals, by leveraging the flexibility of a Software Defined Radio and docker frameworks. The design and deployment aspects of the proposed architecture are discussed, and the performance of the classification algorithm is evaluated. The results of the experimental test campaign on real interfered GNSS signals showed an overall accuracy of 85% and they highlighed the potential for effective, real-time classification of RFIs in GNSS.